Lecture 17: Quasi-Newton methods

Lecture 17: Quasi-Newton methods#

Approximation of the derivative#

  • The formula \(x^{(i+1)} = x^{(i)} - \frac{f(x^{(i)})}{f'(x^{(i)})}\) requires that we are able to compute an expression for the derivative of \(f(x)\).

  • This may not always be possible:

    • the function \(f(x)\) may be a “black box”;

    • the formula for \(f(x)\) may be known but may be difficult for us to differentiate.

  • We can modify Newton’s method by simply approximating \(f'(x^{(i)})\), rather like we approximated \(y'(t)\) when solving a differential equation.

Approximation of \(f'(x)\)#

  • Recall that \(f'(x) = \lim_{\mathrm{d}x \to 0} \frac{f(x + \mathrm{d}x) - f(x)}{\mathrm{d}x}\).

  • Hence we can choose a small value for \(\mathrm{d}x\) (how small?) and approximate:

    \[ f'(x) \approx \frac{f(x + \mathrm{d}x) - f(x)}{\mathrm{d}x}. \]

  • This modified-Newton method then becomes

    \[ x^{(i+1)} = x^{(i)} - \frac{\mathrm{d}x \times f(x^{(i)})}{f(x^{(i)} + \mathrm{d}x) - f(x^{(i)})}. \]

The choice of \(\mathrm{d}x\)#

  • Note that the computational cost of calculating each iterate is about the same as for Newton’s method.

  • What is not obvious however is what choice should be made for the value of \(\mathrm{d}x\).

  • In theory (exact arithmetic) the smaller the choice of \(\mathrm{d}x\) the better the approximation of the derivative.

  • In practice, however, we know that the operation of subtracting two very similar values from each other can lead to significant rounding errors when using floating point arithmetic.

  • Consider the following example…

Problems with floating point arithmetic#

  • When \(f(x) = x^3\) then \(f'(x) = 3 x^2\).

  • Hence, at \(x_0 = 1\), \(f(x_0) = 1\) and \(f'(x_0) = 3\).

  • Consider what happens when we approximate this with python, using finite values for \(\mathrm{d}x\).

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[1], line 29
     24 import pandas as pd
     26 df = pd.DataFrame(data, columns=headers)
     27 df.style.format(
     28     formatter={"dx": "{:e}", "approx": "{:f}", "abs error": "{:e}", "rel error": "{:e}"}
---> 29 ).hide_index().set_caption(
     30     "Simple approximation of a derivative using floating point arithmetic"
     31 )

AttributeError: 'Styler' object has no attribute 'hide_index'

Modified Newton’s method#

  • Recall the definition of machine precision/unit roundoff from Lecture 3.

  • The modified Newton method uses \(\mathrm{d}x = \sqrt{eps}\).

  • For double precision we have

import numpy as np

eps = np.finfo(float).eps
dx = np.sqrt(eps)

x0 = 1.0
df_approx = ((x0 + dx) ** 3 - x0**3) / dx
abs_error = abs(df_approx - 3)
rel_error = abs_error / 3

print(f"{dx=}\n{df_approx=}\n{abs_error=}\n{rel_error=}")

Examples#

  • The example to compute the square root of 2 to a tolerance of \(10^{-12}\) with starting value \(1\) gives \(x^* \approx 1.4142135623731\) after 5 iterations.

  • The naca0012 example starting at 1 with tolerance \(10^{-4}\) gives \(x^* \approx 0.76579\) after 2 iterations.

  • The naca0012 example starting at 1 with tolerance \(10^{-5}\) gives \(x^* \approx 0.765239\) after 3 iterations.

  • The naca0012 example starting at 0.1 with tolerance \(10^{-4}\) gives \(x^* \approx 0.03386\) after 5 iterations.

In each case the performance is almost identical to the Newton method.

The secant method#

  • Suppose we choose \(\mathrm{d}x = x^{(i-1)} - x^{(i)}\), then we get

    \[ f'(x^{(i)}) \approx \frac{f(x^{(i)} + \mathrm{d}x) - f(x^{(i)})}{\mathrm{d}x} = \frac{f(x^{(i)}) - f(x^{(i-1)})}{x^{(i)} - x^{(i-1)}}. \]

  • Newton’s method then becomes

    \[ x^{(i+1)} = x^{(i)} - f(x^{(i)}) \frac{x^{(i)} - x^{(i-1)}}{f(x^{(i)}) - f(x^{(i-1)})} \left(\approx x^{(i)} - \frac{f(x^{(i)})}{f'(x^{(i)})} \right). \]

  • Note that the main advantage of this approach over the previous modified Newton approximation is that only one new evaluation of \(f(x)\) is required per iteration (apart from the very first iteration).

Pros and cons#

Advantages:

  • \(f'(x)\) is not required;

  • only one new evaluation of \(f(x)\) per iteration;

  • still converges almost as quickly as Newton’s method.

Disadvantages:

  • two starting values, \(x^{(0)}\) and \(x^{(1)}\), are required;

  • may require one more iteration than exact Newton (but iterations are cheaper);

  • like Newton’s method the iteration can fail to converge for some starting iterates.

Examples#

  • The example to compute the square root of 2 starting from 1 and 1.5 to a tolerance of \(10^{-4}\) gives the solution as \(x^* \approx 1.4142\) after 3 iterations.

  • The example to computation compound interest start from 100 and 120 to a tolerance of 0.1 gives the solution as \(x^* \approx 235.9\) after 6 iterations.

  • The naca0012 example starting from 1 and 0.9 to a tolerance of \(10^{-4}\) gives the solution as \(x^* \approx 0.7653\) after 3 iterations.

  • The naca0012 example starting from 0 and 0.1 to a tolerance of \(10^{-4}\) gives the solution as \(x^* \approx 0.0339\) after 5 iterations.

Further reading#

The slides used in the lecture are also available

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